{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exploring our data and visulization\n",
    "# Train and create a Linear Regression Model\n",
    "# Evaluate our model\n",
    "# Make Predictions\n",
    "# Calculate Root Means Square Error (RMES)\n",
    "# What if we consider more/less features ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "house = pd.read_csv('home_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "           id             date   price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000         3       2.25         2570   \n",
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       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
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       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
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       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "               id             date   price  bedrooms  bathrooms  sqft_living  \\\n",
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       "21609  6600060120  20150223T000000  400000         4       2.50         2310   \n",
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       "\n",
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       "21608      1131     3.0           0     0  ...      8        1530   \n",
       "21609      5813     2.0           0     0  ...      8        2310   \n",
       "21610      1350     2.0           0     0  ...      7        1020   \n",
       "21611      2388     2.0           0     0  ...      8        1600   \n",
       "21612      1076     2.0           0     0  ...      7        1020   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "21608              0      2009             0    98103  47.6993 -122.346   \n",
       "21609              0      2014             0    98146  47.5107 -122.362   \n",
       "21610              0      2009             0    98144  47.5944 -122.299   \n",
       "21611              0      2004             0    98027  47.5345 -122.069   \n",
       "21612              0      2008             0    98144  47.5941 -122.299   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "21608           1530        1509  \n",
       "21609           1830        7200  \n",
       "21610           1020        2007  \n",
       "21611           1410        1287  \n",
       "21612           1020        1357  \n",
       "\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
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      "date             21613 non-null object\n",
      "price            21613 non-null int64\n",
      "bedrooms         21613 non-null int64\n",
      "bathrooms        21613 non-null float64\n",
      "sqft_living      21613 non-null int64\n",
      "sqft_lot         21613 non-null int64\n",
      "floors           21613 non-null float64\n",
      "waterfront       21613 non-null int64\n",
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      "dtypes: float64(4), int64(16), object(1)\n",
      "memory usage: 3.5+ MB\n"
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    }
   ],
   "source": [
    "house.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.580302e+09</td>\n",
       "      <td>5.400881e+05</td>\n",
       "      <td>3.370842</td>\n",
       "      <td>2.114757</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>3.409430</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.876566e+09</td>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.930062</td>\n",
       "      <td>0.770163</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>0.650743</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.123049e+09</td>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.904930e+09</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.308900e+09</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
       "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
       "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
       "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
       "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
       "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
       "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
       "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
       "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
       "\n",
       "           sqft_lot        floors    waterfront          view     condition  \\\n",
       "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
       "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
       "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
       "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
       "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
       "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
       "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
       "\n",
       "              grade    sqft_above  sqft_basement      yr_built  yr_renovated  \\\n",
       "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
       "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
       "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
       "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
       "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
       "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
       "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
       "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
       "\n",
       "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
       "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
       "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
       "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
       "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
       "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
       "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
       "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
       "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
       "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
       "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
       "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price of House')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter(house.sqft_living,house.price)\n",
    "plt.xlabel('sqft of House')\n",
    "plt.ylabel('Price of House')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17ffa2de708>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(house.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17ffa2f9b48>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(house['price'],color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17ffa206448>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='zipcode',y='price',data=house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = house[['bedrooms','bathrooms','sqft_living','sqft_lot','floors',\n",
    "           'sqft_above','sqft_lot15','yr_built','condition','zipcode']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = house['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21613, 10), (21613,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training , Testing  75/25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16209, 10) (16209,)\n",
      "(5404, 10) (5404,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape , y_train.shape)\n",
    "print(X_test.shape , y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.721</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date   price  bedrooms  bathrooms  sqft_living  \\\n",
       "1  6414100192  20141209T000000  538000         3       2.25         2570   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode     lat     long  sqft_living15  sqft_lot15  \n",
       "1      1951          1991    98125  47.721 -122.319           1690        7639  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house1 = house[house['id'] == 6414100192]\n",
    "house1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242392.5433738893"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17ffcca1288>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "plt.scatter(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.86787300e+04,  6.50420543e+04,  2.89157700e+02,  9.38917798e-03,\n",
       "        4.69476414e+04,  2.29800354e+01, -6.06551212e-01, -3.21094963e+03,\n",
       "        2.19505966e+04,  1.23774470e+02])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coeffic value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>-68678.729971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>65042.054315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>289.157700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>0.009389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>46947.641390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>22.980035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>-0.606551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>-3210.949631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>21950.596570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>123.774470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             coeffic value\n",
       "bedrooms     -68678.729971\n",
       "bathrooms     65042.054315\n",
       "sqft_living     289.157700\n",
       "sqft_lot          0.009389\n",
       "floors        46947.641390\n",
       "sqft_above       22.980035\n",
       "sqft_lot15       -0.606551\n",
       "yr_built      -3210.949631\n",
       "condition     21950.596570\n",
       "zipcode         123.774470"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_coef = pd.DataFrame(model.coef_,X.columns,columns = ['coeffic value'])\n",
    "df_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5957070.513787943"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RMS Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58214449156.36607"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = metrics.mean_squared_error(y_test,prediction)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241276.70661787075"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE = np.sqrt(mse)\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = house[['bedrooms','bathrooms','sqft_living','sqft_lot','yr_built','zipcode']]\n",
    "y = house['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = LinearRegression()\n",
    "model2.fit(X_train,y_train)\n",
    "prediction2 = model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242392.5433738893, 718281.207960289)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction[1], prediction2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61631580368.51299"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse2 = metrics.mean_squared_error(y_test,prediction2)\n",
    "mse2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248257.08523325773"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE2 = np.sqrt(mse2)\n",
    "RMSE2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241276.70661787075"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE"
   ]
  }
 ],
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